P.148 Perceptions of frailty in spinal metastatic disease: international survey of the AO spine community
Bibliographic record
Abstract
Background: Frailty is increasingly recognized for an association with adverse events, mortality, and hospital discharge disposition among surgical patients. The purpose of this study was to describe how spinal surgeons conceptualize, define, and assess frailty in the context of spinal metastatic disease (SMD). Methods: We conducted an international, cross-sectional, 33-question survey of the AO Spine community. The survey was developed using a modified Delphi technique and was designed to elucidate preoperative surrogate markers of frailty in the context of SMD. Responses were ranked using weighted averages. Consensus was defined as ≥ 70% agreement among respondents. Results: Results were analyzed for 312 respondents (86% completion rate). Study participants represented 71 countries. Most respondents informally assess frailty in patients with SMD by forming a general perception based on clinical condition and patient history. Consensus was attained regarding the association between 14 clinical variables and frailty. Severe comorbidities, systemic disease burden, and poor performance status were most associated with frailty; severe comorbidities included high-risk cardio-pulmonary disease, renal failure, liver failure, and malnutrition. Conclusions: Surgeons recognized frailty is important but commonly evaluate it based on general clinical impression rather than using existing frailty tools. We identified preoperative surrogate markers of frailty perceived as most relevant in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".